The Four Stages of Digital Adoption Within Supply Chains
According to data contained in “Elevating Supply Chain Digital Consciousness”, the 2019 MHI Annual Industry Report, successful digital adoption within supply chains is an evolutionary process that consists of four distinct stages of digital adoption within Supply Chains.
Stage 1: Digital Connectivity – Data from multiple sources, internal and external, is collected, cleansed, and organized. Converting the amassed real-time, visible data into a usable form creates a strong foundation for the digital supply chain.
Stage 2: Automation – The use of automated systems, robotics, and augmented technologies to perform repetitive, resource-intensive supply chain tasks, to streamline operations and make them safer, quicker, and more reliable.
Equipment commonly used in this process includes:
- Robotics and automation
- Autonomous vehicles and drones
- Wearable and mobile technology
- 3D printing (additive manufacturing)
Stage 3: Advanced Analytics – The conversion of all of the massed data into useful, actionable insights, because big data without analytics can actually hinder the digital adoption process.
The tools and techniques that are usually employed in this process are:
- Inventory and network optimization tools
- Predictive analytics
- Prescriptive analytics
Stage 4: Artificial Intelligence (AI) – Defined by the Deloitte Analytics Academy as “decision-making enabled by machine learning” (computers learning on-the-fly from data, rather than being pre-programmed to follow a fixed set of rules).
Artificial Intelligence takes the digital information collected, organized and processed during the first three stages and turns it into smart insights. At the same time, AI itself becomes smarter itself as it learns from the patterns, behaviors, and feedback provided to it by the various digital and human interfaces.
As such, AI can be thought of as the ultimate evolution of business intelligence and analytics.